MSⁿLib: efficient generation of open multi-stage fragmentation mass spectral libraries

Untargeted high-resolution mass spectrometry is a key tool in clinical metabolomics, natural product discovery and exposomics, with compound identification remaining the major bottleneck. Currently, the standard workflow applies spectral library matching against tandem mass spectrometry (MS²) fragmentation data. Multi-stage fragmentation (MSⁿ) yields more profound insights into substructures, enabling validation of fragmentation pathways; however, the community lacks open MSⁿ reference data of diverse natural products and other chemicals. Here we describe MSⁿLib, a machine learning-ready open resource of >2 million spectra in MSⁿ trees of 30,008 unique small molecules, built with a high-throughput data acquisition and processing pipeline in the open-source software mzmine.